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At present, research of Mesos is in the early stage. Because of the two-level hierarchical scheduling, Mesos doesn't take the whole cluster into consideration in resource scheduling, which could cause load imbalance and low resource utilization. To solve above-mentioned problem, this paper proposed a resource adjustment plan based on an improved artificial fish swarm algorithm, it adopts a new behavior...
For multiuser massive MIMO systems, the acquisition and utilization of statistical channel information is very important. In this paper, we first adopt PASTd algorithm to track the uplink dominant eigenvectors (sub-eigenspace) of channel covariance matrix and then present a low-complexity algorithm to transform the uplink eigenvectors to the downlink eigenvectors. Thirdly, two scheduling algorithms...
The important task of correcting label noise is addressed infrequently in literature. The difficulty of developing a robust label correction algorithm leads to this silence concerning label correction. To break the silence, we propose two algorithms to correct label noise. One utilizes self-training to re-label noise, called Self-Training Correction (STC). Another is a clustering-based method, which...
Classification of microarray data has always been a challenging task due to the enormous number of genes. Finding a small, closely related gene set to accurately classify disease cells is an important research problem. Integrating biological knowledge into genomic analysis to help to improve the interpretation of the results is an effective approach. In this paper, affinity propagation (AP) clustering...
Sleep Scheduling is one of the most important challenges for energy conservation in Wireless Sensor Networks (WSNs). Connected Dominating Set (CDS) based virtual backbone is often employed in WSNs. Compared with the ordinary CDS, the R-hop Connected Dominating Set (R-CDS) with smaller size is more suitable for build a virtual backbone for energy conservation. In this paper, a R-hop connected dominating...
Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules are an important and active area of research in medical image processing. At present, the classical segmentation algorithm of pulmonary nodules can not accurately obtain the boundary information of pulmonary nodules. In order to solve the problem, a new...
A latency-hiding algorithm for the parallelization of large scale agent-based model simulations (ABMS) on parallel/distributed computing platform is proposed. The key idea of this algorithm is using redundant computations to hide communication latencies. An analytical model for this algorithm is presented to tell how to select R value to reach the best speedup. Compared to B+2R algorithm [1], theoretical...
Clustering is one of the most popular methods for data analysis, which is prevalent in many disciplines such as image segmentation, bioinformatics, pattern recognition and statistics etc. The most popular and simplest clustering algorithm is K-means because of its easy implementation, simplicity, efficiency and empirical success. However, the real-world applications produce huge volumes of data, thus,...
An improved BP neural network classifier integration method was mainly described, by which using k-means clustering a group of value of weights and thresholds with some differences were gotten, and then as the value of individuals of integrated network to improve the performance of integrated learning, and be successfully applied to non-specific human isolated word speech recognition system. By comparing...
Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first intuition about their data by identifying meaningful groups among the data objects. K-means is one of the most famous clustering algorithms. Its simplicity and speed allow it to run on large data sets. However,...
To develop a computer aided detection system that improves diagnostic accuracy of lung cancer on thoracic CT. A novel method for CT image segmentation of lung is proposed, with which, several regions that are suspicious of cancer can be segmented rapidly and effectively. Algorithm of extracting pulmonary parenchyma was introduced firstly to extract pulmonary parenchyma. Secondly, FCM was employed...
In the field of data mining, clustering is one of the important methods. K-Means is a typical distance-based clustering algorithm; 2-tier clustering should implement scalable clustering by means of dividing, sampling and knowledge integrating. Among those tools of distributed processing, Map-Reduce has been widely embraced by both academia and industry. Hadoop is an open-source parallel and distributed...
Spam, also known as unsolicited bulk email (UBE), is becoming increasingly harmful for email traffics. Filtering is a simple and efficient way to combat against spam. Machine-learning-based classification algorithms are of excellent performance in filtering spam. However, the classifiers need be trained with a group of training samples before being able to work. Heavy manual labor and privacy problems...
Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose...
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